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159,551
The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
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Cited by 13236 (32 self)
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theoretical and algorithmic aspects of the theory. The goal of this overview is to demonstrate how the abstract learning theory established conditions for generalization which are more general than those discussed in classical statistical paradigms and how the understanding of these conditions inspired new
Kmeans++: The advantages of careful seeding.
 In Proceedings of the Eighteenth Annual ACMSIAM Symposium on Discrete Algorithms, SODA ’07,
, 2007
"... Abstract The kmeans method is a widely used clustering technique that seeks to minimize the average squared distance between points in the same cluster. Although it offers no accuracy guarantees, its simplicity and speed are very appealing in practice. By augmenting kmeans with a very simple, ran ..."
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Cited by 478 (8 self)
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Abstract The kmeans method is a widely used clustering technique that seeks to minimize the average squared distance between points in the same cluster. Although it offers no accuracy guarantees, its simplicity and speed are very appealing in practice. By augmenting kmeans with a very simple
Constrained Kmeans Clustering with Background Knowledge
 In ICML
, 2001
"... Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular kmeans clustering algorithm can be pro tably modi ed ..."
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Cited by 488 (9 self)
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Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular kmeans clustering algorithm can be pro tably modi ed
An Efficient kMeans Clustering Algorithm: Analysis and Implementation
, 2000
"... Kmeans clustering is a very popular clustering technique, which is used in numerous applications. Given a set of n data points in R d and an integer k, the problem is to determine a set of k points R d , called centers, so as to minimize the mean squared distance from each data point to its ..."
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Cited by 417 (4 self)
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nearest center. A popular heuristic for kmeans clustering is Lloyd's algorithm. In this paper we present a simple and efficient implementation of Lloyd's kmeans clustering algorithm, which we call the filtering algorithm. This algorithm is very easy to implement. It differs from most other
Genetic KMeans Algorithm – Implementation and Analysis
"... Abstract: Kmeans algorithm is most widely used algorithm for unsupervised clustering problem. Though it is accepted but it has some problems which make it unreliable. Initialization of the random cluster centres, number of clusters and terminating condition play a major role in quality of clusteri ..."
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Abstract: Kmeans algorithm is most widely used algorithm for unsupervised clustering problem. Though it is accepted but it has some problems which make it unreliable. Initialization of the random cluster centres, number of clusters and terminating condition play a major role in quality
Xmeans: Extending Kmeans with Efficient Estimation of the Number of Clusters
 In Proceedings of the 17th International Conf. on Machine Learning
, 2000
"... Despite its popularity for general clustering, Kmeans suffers three major shortcomings; it scales poorly computationally, the number of clusters K has to be supplied by the user, and the search is prone to local minima. We propose solutions for the first two problems, and a partial remedy for the t ..."
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Cited by 418 (5 self)
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) measure. The innovations include two new ways of exploiting cached sufficient statistics and a new very efficient test that in one Kmeans sweep selects the most promising subset of classes for refinement. This gives rise to a fast, statistically founded algorithm that outputs both the number of classes
Research on Kmeans clustering algorithm and its implementation
"... Abstract—Kmeans algorithm is a kind of clustering analysis based on partition algorithm, it through constant iteration to clustering, when algorithm converges to an end conditions, and the output iterative process termination clustering results. Because its algorithm is simple, and easy to realize ..."
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Abstract—Kmeans algorithm is a kind of clustering analysis based on partition algorithm, it through constant iteration to clustering, when algorithm converges to an end conditions, and the output iterative process termination clustering results. Because its algorithm is simple, and easy to realize
A Proposed Modification of KMeans Algorithm
"... Abstract—Kmeans algorithm is one of the most popular algorithms for data clustering. With this algorithm, data of similar types are tried to be clustered together from a large data set with brute force strategy which is done by repeated calculations. As a result, the computational complexity of thi ..."
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Abstract—Kmeans algorithm is one of the most popular algorithms for data clustering. With this algorithm, data of similar types are tried to be clustered together from a large data set with brute force strategy which is done by repeated calculations. As a result, the computational complexity
A comparison of document clustering techniques
 In KDD Workshop on Text Mining
, 2000
"... This paper presents the results of an experimental study of some common document clustering techniques: agglomerative hierarchical clustering and Kmeans. (We used both a “standard” Kmeans algorithm and a “bisecting ” Kmeans algorithm.) Our results indicate that the bisecting Kmeans technique is ..."
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Cited by 613 (27 self)
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This paper presents the results of an experimental study of some common document clustering techniques: agglomerative hierarchical clustering and Kmeans. (We used both a “standard” Kmeans algorithm and a “bisecting ” Kmeans algorithm.) Our results indicate that the bisecting Kmeans technique
A new hybrid clustering algorithm based on Kmeans and ant colony algorithm
"... Abstract—Kmeans algorithm and ant clustering algorithm are all traditional algorithms. The two algorithms can complement each other. The combination of two algorithms will improve clustering’s accuracy and speed up algorithm ’ convergence. Tests prove hybrid clustering algorithm is more effective t ..."
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Abstract—Kmeans algorithm and ant clustering algorithm are all traditional algorithms. The two algorithms can complement each other. The combination of two algorithms will improve clustering’s accuracy and speed up algorithm ’ convergence. Tests prove hybrid clustering algorithm is more effective
Results 1  10
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159,551